Corrected Continuous Correlation Filter for Long-Term Tracking

Abstract

Recently, a large number of visual tracking algorithms based on discriminative correlation filter have been proposed with demonstrated success. However, most of algorithms cannot well handle long-term videos in which the locating error may accumulate and lead to drifting or tracking failure. Hence, it is of great importance to design a robust long-term tracker which can effectively alleviate tracking drift and redetect the object in case of tracking failure. In this paper, a continuous correlation filter has been proposed to achieve subpixel object locations in continuous domain. For scale estimation, we present a novel multipyramid strategy and the optimal scale tracker is used to correct object locating error in return. Meanwhile, we learn an online random fern classifier to redetect the target in case of tracking failure. By analyzing the confidence of predicted location, we update the translation model conservatively by the reliable targets throughout the sequence. To evaluate the proposed algorithm, extensive experiments are conducted on a benchmark with 100 video sequences, which demonstrate that our tracking mechanism is well fit to tackle long-term sequences and outperforms the state-of-the-art methods.